Extracting and Visualizing Stock Data
Description
Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.
Table of Contents
- Define a Function that Makes a Graph
- Question 1: Use yfinance to Extract Stock Data
- Question 2: Use Webscraping to Extract Tesla Revenue Data
- Question 3: Use yfinance to Extract Stock Data
- Question 4: Use Webscraping to Extract GME Revenue Data
- Question 5: Plot Tesla Stock Graph
- Question 6: Plot GameStop Stock Graph
Estimated Time Needed: 30 min
Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
Requirement already satisfied: yfinance in /opt/conda/lib/python3.12/site-packages (0.2.61) Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.3) Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.6) Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.32.3) Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.0.11) Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.3.6) Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2024.2) Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.6) Requirement already satisfied: peewee>=3.16.2 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.18.1) Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.12.3) Requirement already satisfied: curl_cffi>=0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.11.1) Requirement already satisfied: protobuf>=3.19.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (6.31.1) Requirement already satisfied: websockets>=13.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (15.0.1) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5) Requirement already satisfied: cffi>=1.12.0 in /opt/conda/lib/python3.12/site-packages (from curl_cffi>=0.7->yfinance) (1.17.1) Requirement already satisfied: certifi>=2024.2.2 in /opt/conda/lib/python3.12/site-packages (from curl_cffi>=0.7->yfinance) (2024.12.14) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2025.2) Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.4.1) Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2.3.0) Requirement already satisfied: pycparser in /opt/conda/lib/python3.12/site-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.22) Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0) Requirement already satisfied: bs4 in /opt/conda/lib/python3.12/site-packages (0.0.2) Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.12/site-packages (from bs4) (4.12.3) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4->bs4) (2.5) Requirement already satisfied: nbformat in /opt/conda/lib/python3.12/site-packages (5.10.4) Requirement already satisfied: fastjsonschema>=2.15 in /opt/conda/lib/python3.12/site-packages (from nbformat) (2.21.1) Requirement already satisfied: jsonschema>=2.6 in /opt/conda/lib/python3.12/site-packages (from nbformat) (4.23.0) Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.7.2) Requirement already satisfied: traitlets>=5.1 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.14.3) Requirement already satisfied: attrs>=22.2.0 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (25.1.0) Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (2024.10.1) Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.36.2) Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.22.3) Requirement already satisfied: platformdirs>=2.5 in /opt/conda/lib/python3.12/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.6) Requirement already satisfied: typing-extensions>=4.4.0 in /opt/conda/lib/python3.12/site-packages (from referencing>=0.28.4->jsonschema>=2.6->nbformat) (4.12.2) Requirement already satisfied: plotly in /opt/conda/lib/python3.12/site-packages (6.1.2) Requirement already satisfied: narwhals>=1.15.1 in /opt/conda/lib/python3.12/site-packages (from plotly) (1.41.0) Requirement already satisfied: packaging in /opt/conda/lib/python3.12/site-packages (from plotly) (24.2)
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.io as pio
pio.renderers.default = "iframe"
In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.
import warnings
warnings.filterwarnings("ignore", category = FutureWarning)
Define Graphing Function¶
In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
from IPython.display import display, HTML
fig_html = fig.to_html()
display(HTML(fig_html))
Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.
Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.
Question 1: Use yfinance to Extract Stock Data¶
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.
import yfinance as yf
tesla = yf.Ticker("TSLA")
Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.
tesla_data = tesla.history(period ="max")
Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.
tesla_data.reset_index(inplace = True)
tesla_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 00:00:00-04:00 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0.0 | 0.0 |
| 1 | 2010-06-30 00:00:00-04:00 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0.0 | 0.0 |
| 2 | 2010-07-01 00:00:00-04:00 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0.0 | 0.0 |
| 3 | 2010-07-02 00:00:00-04:00 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0.0 | 0.0 |
| 4 | 2010-07-06 00:00:00-04:00 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0.0 | 0.0 |
Question 2: Use Webscraping to Extract Tesla Revenue Data¶
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.
import requests
url = " https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text
Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')
Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.
Step-by-step instructions
Here are the step-by-step instructions:
1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame
Click here if you need help locating the table
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
We are focusing on quarterly revenue in the lab.
!pip install pandas
import pandas as pd
from bs4 import BeautifulSoup
import requests
# 1: Create an Empty DataFrame
tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])
# 2: Fetch the HTML content
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text
# 3: Parse HTML with BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')
# 4: Find the Relevant Table
table = soup.find_all("tbody")[1] # Assuming the table is at index 1
# 5: Iterate Through Rows in the Table Body
rows = []
for row in table.find_all("tr"):
# Step 6: Extract Data from Columns
columns = row.find_all("td")
if len(columns) > 1:
date = columns[0].text.strip()
revenue = columns[1].text.strip().replace(',', '').replace('$', '')
# Collect data in a list
rows.append({"Date": date, "Revenue": revenue})
# 7: Convert list to DataFrame
tesla_revenue = pd.DataFrame(rows)
# Display the DataFrame
print(tesla_revenue)
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Date Revenue
0 2022-09-30 21454
1 2022-06-30 16934
2 2022-03-31 18756
3 2021-12-31 17719
4 2021-09-30 13757
5 2021-06-30 11958
6 2021-03-31 10389
7 2020-12-31 10744
8 2020-09-30 8771
9 2020-06-30 6036
10 2020-03-31 5985
11 2019-12-31 7384
12 2019-09-30 6303
13 2019-06-30 6350
14 2019-03-31 4541
15 2018-12-31 7226
16 2018-09-30 6824
17 2018-06-30 4002
18 2018-03-31 3409
19 2017-12-31 3288
20 2017-09-30 2985
21 2017-06-30 2790
22 2017-03-31 2696
23 2016-12-31 2285
24 2016-09-30 2298
25 2016-06-30 1270
26 2016-03-31 1147
27 2015-12-31 1214
28 2015-09-30 937
29 2015-06-30 955
30 2015-03-31 940
31 2014-12-31 957
32 2014-09-30 852
33 2014-06-30 769
34 2014-03-31 621
35 2013-12-31 615
36 2013-09-30 431
37 2013-06-30 405
38 2013-03-31 562
39 2012-12-31 306
40 2012-09-30 50
41 2012-06-30 27
42 2012-03-31 30
43 2011-12-31 39
44 2011-09-30 58
45 2011-06-30 58
46 2011-03-31 49
47 2010-12-31 36
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
51 2009-12-31
52 2009-09-30 46
53 2009-06-30 27
Execute the following line to remove the comma and dollar sign from the Revenue column.
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(',', '').str.replace('$', '')
Execute the following lines to remove an null or empty strings in the Revenue column.
# Remove null values
tesla_revenue.dropna(inplace=True)
# Remove empty strings
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.
print(tesla_revenue.tail())
Date Revenue 48 2010-09-30 31 49 2010-06-30 28 50 2010-03-31 21 52 2009-09-30 46 53 2009-06-30 27
Question 3: Use yfinance to Extract Stock Data¶
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.
!pip install yfinance
import yfinance as yf
gme = yf.Ticker("GME")
Requirement already satisfied: yfinance in /opt/conda/lib/python3.12/site-packages (0.2.61) Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.3) Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.6) Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.32.3) Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.0.11) Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.3.6) Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2024.2) Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.6) Requirement already satisfied: peewee>=3.16.2 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.18.1) Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.12.3) Requirement already satisfied: curl_cffi>=0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.11.1) Requirement already satisfied: protobuf>=3.19.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (6.31.1) Requirement already satisfied: websockets>=13.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (15.0.1) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5) Requirement already satisfied: cffi>=1.12.0 in /opt/conda/lib/python3.12/site-packages (from curl_cffi>=0.7->yfinance) (1.17.1) Requirement already satisfied: certifi>=2024.2.2 in /opt/conda/lib/python3.12/site-packages (from curl_cffi>=0.7->yfinance) (2024.12.14) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2025.2) Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.4.1) Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2.3.0) Requirement already satisfied: pycparser in /opt/conda/lib/python3.12/site-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.22) Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)
Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.
gme_data = gme.history(period = "max")
print(gme_data)
Open High Low Close \
Date
2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667
2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250
2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834
2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504
2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210
... ... ... ... ...
2025-05-23 00:00:00-04:00 30.600000 33.209999 30.549999 33.029999
2025-05-27 00:00:00-04:00 33.970001 35.740002 33.630001 35.009998
2025-05-28 00:00:00-04:00 35.779999 35.810001 30.730000 31.209999
2025-05-29 00:00:00-04:00 31.170000 31.350000 29.320000 29.570000
2025-05-30 00:00:00-04:00 29.200001 30.490000 29.190001 29.799999
Volume Dividends Stock Splits
Date
2002-02-13 00:00:00-05:00 76216000 0.0 0.0
2002-02-14 00:00:00-05:00 11021600 0.0 0.0
2002-02-15 00:00:00-05:00 8389600 0.0 0.0
2002-02-19 00:00:00-05:00 7410400 0.0 0.0
2002-02-20 00:00:00-05:00 6892800 0.0 0.0
... ... ... ...
2025-05-23 00:00:00-04:00 30477600 0.0 0.0
2025-05-27 00:00:00-04:00 33217800 0.0 0.0
2025-05-28 00:00:00-04:00 45300400 0.0 0.0
2025-05-29 00:00:00-04:00 15819000 0.0 0.0
2025-05-30 00:00:00-04:00 10189100 0.0 0.0
[5862 rows x 7 columns]
Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.
gme_data.reset_index(inplace = True)
gme_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 00:00:00-05:00 | 1.620128 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 00:00:00-05:00 | 1.683251 | 1.687459 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 00:00:00-05:00 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Question 4: Use Webscraping to Extract GME Revenue Data¶
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.
import requests
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
response = requests.get(url)
html_data2 = response.text
Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_data2, 'html.parser')
Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.
Note: Use the method similar to what you did in question 2.
Click here if you need help locating the table
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
gme_revenue = []
# Find the second table body (index 1)
table = soup.find_all("tbody")[1]
rows = table.find_all("tr")
# Loop through rows and extract data
for row in rows:
cols = row.find_all("td")
if len(cols) == 2:
date = cols[0].text.strip()
revenue = cols[1].text.strip().replace('$', '').replace(',', '')
if revenue != '':
gme_revenue.append({'Date': date, 'Revenue': revenue})
# Convert list to DataFrame
gme_revenue = pd.DataFrame(gme_revenue)
# Optional: Convert Revenue to float
gme_revenue['Revenue'] = gme_revenue['Revenue'].astype(float)
# Display
gme_revenue.head()
| Date | Revenue | |
|---|---|---|
| 0 | 2020-04-30 | 1021.0 |
| 1 | 2020-01-31 | 2194.0 |
| 2 | 2019-10-31 | 1439.0 |
| 3 | 2019-07-31 | 1286.0 |
| 4 | 2019-04-30 | 1548.0 |
Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.
gme_revenue.tail()
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667.0 |
| 58 | 2005-10-31 | 534.0 |
| 59 | 2005-07-31 | 416.0 |
| 60 | 2005-04-30 | 475.0 |
| 61 | 2005-01-31 | 709.0 |
Question 5: Plot Tesla Stock Graph¶
Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.
Hint
You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.
make_graph(tesla_data, tesla_revenue, 'Tesla')
/tmp/ipykernel_302/109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipykernel_302/109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
Question 6: Plot GameStop Stock Graph¶
Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.
Hint
You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`
make_graph(gme_data, gme_revenue, 'GameStop')
/tmp/ipykernel_302/109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipykernel_302/109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
About the Authors:
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Azim Hirjani
Change Log¶
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
|---|---|---|---|
| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |
| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |
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